Meo and Pinardi had the original idea. Salis and Sartor performed all the experiments. All the authors contributed with discussions to the writing of the article.
Version 1
: Received: 1 August 2023 / Approved: 4 August 2023 / Online: 4 August 2023 (09:33:20 CEST)
Ferraris, S.; Meo, R.; Pinardi, S.; Salis, M.; Sartor, G. Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire. Sensors2023, 23, 7632.
Ferraris, S.; Meo, R.; Pinardi, S.; Salis, M.; Sartor, G. Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire. Sensors 2023, 23, 7632.
Ferraris, S.; Meo, R.; Pinardi, S.; Salis, M.; Sartor, G. Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire. Sensors2023, 23, 7632.
Ferraris, S.; Meo, R.; Pinardi, S.; Salis, M.; Sartor, G. Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire. Sensors 2023, 23, 7632.
Abstract
Machine Learning can be used for social good. In this paper, we discuss how it can be used to combat climate change and facilitate land management and farming in developing countries and in particular in Côte d’Ivoire. This paper explores models that improve land and water management and agricultural farming cultivation to contrast climate change. Côte d’Ivoire is the largest producer of cocoa beans (43%) in the world, but deforestation, lack of rainfall, drought, and climate change threaten crops and the already fragile economy of Ivorian farmers. It is important to combat climate change with methods and techniques that are affordable to the local farmers and also induce positive effects in production. We discuss the use of low-cost sensors to collect data on the soil and open data and open source software to develop AI tools. We show that using deep neural networks (YOLOv5m) is effective for detecting healthy plants and pods of cocoa from damaged ones only using mobile phone images. Focusing on a single land is not enough to combat climate change, which has different causes and involves also knowledge at a higher scale. We propose a new method of forecasting for the analysis of remote sensors. Remote sensor data come from GRACE NASA Mission and ERA5 produced by the Copernicus Climate Change Service at ECMWF. We implement a new deep neural network architecture named CIWA-net. It is based on a Fully Convolutional Neural Network (FCN) [ 1] and it is a U-net like architecture [2]. The aim of CIWA-net is to forecast Total Water Storage Anomalies (TWSA). We show the quality of our model with a comparison to a vanilla Convolutional Neural Network. CIWA-net could be used also for the detection of lands that interfere with agricultural work and yields, such as deserted areas, water-soaking soil areas, zones at risk of desertification, and poor land use. The employment of AI at the service of agriculture can decrease crop losses and waste, lower the inputs onto the soil of fertilizers, responsible for the increase of Greenhouse Gases. It could be useful to help the small farmers (at a local scale) and also the policy-makers and farmers’ cooperatives (at the regional scale) to take valid and coordinated countermeasures to improve the correct use of the lands, helping to contrast and adapt to climate change.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
